Note: The job is a remote job and is open to candidates in USA. PODS is building an Operations Data Science & AI team to turn operational expertise into automated, optimized decision systems. The Data Scientist II will develop optimization and predictive models, automate data workflows, maintain Snowflake data models, and create decision-support tools for capacity planning, routing, scheduling, resource allocation, and other field operations.
Responsibilities
- Build and support optimization models for capacity planning, routing, scheduling, and resource allocation
- Formulate business problems using decision variables, objectives, and operational constraints
- Assist in root-cause analysis to surface optimization and automation opportunities across field operations
- Build, test, and maintain forecasting, regression, classification, and anomaly-detection models for operational problems
- Prepare and validate data, engineer features, and evaluate model results
- Build reproducible data pipelines and automate recurring analyses, model runs, and reporting, replacing manual processes
- Contribute to shared tooling, frameworks, and standards so that solutions are repeatable
- Maintain data models in Snowflake that other analysts and downstream tools rely on
- Create dashboards and decision-support tools that make results actionable
- Document logic, methodology, and assumptions alongside every model, tool, or pipeline you build
- Present findings and their limitations in plain language to the team and operational stakeholders
Skills
- Hands-on experience formulating and solving mixed-integer linear programming models, including defining decision variables, objectives, and constraints
- Previous experience with Gurobi, Pyomo, OR-Tools, PuLP, CPLEX, or a similar optimization library or solver is required
- Strong SQL on a modern cloud data warehouse, preferably Snowflake, and Python for analysis and model development
- Experience building, testing, and validating forecasting, regression, classification, or other predictive models, with judgment about which method fits the problem
- Experience building reproducible data pipelines and automating recurring analyses and model workflows
- Ability to communicate analytical and model outputs through clear visualizations and practical decision-support tools
- Ability to explain methods and results clearly and document work so that it is reproducible and reviewable
- Ability to turn loosely defined operational problems into clear analytical questions and practical solutions
- Bachelor's degree in a quantitative field such as Data Science, Statistics, Operations Research, Industrial Engineering, Applied Mathematics, Physics, Computer Science, Engineering, Economics, or a related field required
- 3+ years of applied data science, machine learning, or quantitative analytics experience. Relevant internship, co-op, or graduate research may count toward experience
- Hands-on experience with SQL on a modern cloud data warehouse (Snowflake preferred) and with Python for analysis
- Experience or coursework in machine learning and mathematical optimization, with exposure to cloud-based data platforms such as Snowflake or AWS
- Ability to sit at a desk and use a computer for up to 8 hours a day; Ability to use hands and fingers to type on a keyboard and use a mouse to navigate; Vision sufficient to view small details on a computer monitor
- Ability to stand and walk up to 8 hours a day; ability to stoop, bend and lift boxes weighing up to 50 lbs
- Ability to hear and verbally communicate using a telephone handset and/or connected headset device
- Master's degree
- Experience supporting an Operations, Supply Chain, logistics, or other capacity-constrained business is a plus
Benefits
- Remote work arrangement
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